图 书 馆开课单位--斯坦福大学
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101Rich Probabilistic Models for Holistic Scene Understanding[整体场景理解的丰富概率模型]
Daphne Koller(斯坦福大学) Research focuses on using probabilistic models and machine learning to understand complex domains that involve large amounts of uncertainty.
热度：92
Daphne Koller(斯坦福大学) Research focuses on using probabilistic models and machine learning to understand complex domains that involve large amounts of uncertainty.
热度：92
102Identity Management On Homogeneous spaces[齐性空间的身份管理]
Xiaoye Jiang; Leonidas J. Guibas(斯坦福大学) We consider the identity management problem, where the identities are classified into two classes, red and blue. The purpose here is to make predictio...
热度：54
Xiaoye Jiang; Leonidas J. Guibas(斯坦福大学) We consider the identity management problem, where the identities are classified into two classes, red and blue. The purpose here is to make predictio...
热度：54
103Empirical Comparison of Algorithms for Network Community Detection[对于网络社区检测算法的实证比较]
Jure Leskovec(斯坦福大学) Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In pr...
热度：47
Jure Leskovec(斯坦福大学) Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In pr...
热度：47
104Predicting Positive and Negative Links in Online Social Networks[预测在线社交网络正负链接]
Jure Leskovec(斯坦福大学) We study online social networks in which relationships can be both positive (indicating friendship) and negative (indicating opposition or antagonism)...
热度：59
Jure Leskovec(斯坦福大学) We study online social networks in which relationships can be both positive (indicating friendship) and negative (indicating opposition or antagonism)...
热度：59
105An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators[生成，歧视性的渐近分析，和pseudolikelihood估计]
Percy Liang(斯坦福大学) Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudol...
热度：63
Percy Liang(斯坦福大学) Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudol...
热度：63
106Lecture 4: Project Subgradient For Dual Problem[讲座4：对偶问题的次梯度项目]
Boyd Stephen P(斯坦福大学) Sure, we’re going to need strong duality holds if it were strictly feasible. We’d have Slater’s condition and strong duality would h...
热度：65
Boyd Stephen P(斯坦福大学) Sure, we’re going to need strong duality holds if it were strictly feasible. We’d have Slater’s condition and strong duality would h...
热度：65
107Lecture 16: Continue On Unconstrained Minimization[讲座16：继续进行无约束的最小化 ]
Boyd Stephen P(斯坦福大学) Let me make an announcement. Actually, the announcement has to do with Homework 8, which we will assign later today. So we are still doing last debugg...
热度：49
Boyd Stephen P(斯坦福大学) Let me make an announcement. Actually, the announcement has to do with Homework 8, which we will assign later today. So we are still doing last debugg...
热度：49
108Lecture 3: Convergence Proof[讲座3：收敛性的证明]
Boyd Stephen P(斯坦福大学) Okay, if there’s no questions about last time, then I think we’ll just jump in and start in on subgradient methods. So far subgradient met...
热度：57
Boyd Stephen P(斯坦福大学) Okay, if there’s no questions about last time, then I think we’ll just jump in and start in on subgradient methods. So far subgradient met...
热度：57
109Lecture 24: Principles of Good Software Engineering for Managing Large Amounts of Data[讲座24：用于管理大量数据的良好软件工程原则]
Mehran Sahami(斯坦福大学) So a couple quick announcements before we get into things. One is there is one handout, which is your section handout for this week. And kind of one o...
热度：42
Mehran Sahami(斯坦福大学) So a couple quick announcements before we get into things. One is there is one handout, which is your section handout for this week. And kind of one o...
热度：42
110Information-Theoretic Metric Learning[信息论度量学习 ]
Jason Davis(斯坦福大学) We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints ...
热度：301
Jason Davis(斯坦福大学) We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints ...
热度：301